1. Field of Invention
The present invention generally relates to systems and methods for providing decision support predictive indicators that will create real-time probability impact information for decision makers and logistical managers when allocating resources and man power responsive to infrastructure repair, emergency events and/or natural resource issues, and more particularly to the impact of weather events on infrastructure, natural resources and/or environmental conditions.
2. Background of Art
Emergency Response Management is often the task of government agencies, utilities and businesses who are charged with responding in many crisis situations. The Federal Government utilizes the Federal Emergency Management Agency (FEMA) to respond to major catastrophes, such as hurricanes, earthquakes, nuclear accidents, tornadoes, wildfires and the like. On a more local level, utility companies tasked with providing essential services to consumers (power, water, gas, sewage) have created internal Emergency Management departments with the goal of responding quickly to outages often caused by weather events. In private industry, businesses which handle chemicals and other possibly harmful materials generally have created similar internal Emergency Management departments which deal with chemical spills and leaks and other environmental hazards.
However, often times such emergencies are created and/or exacerbated by weather conditions. Certainly, hurricanes and tornadoes are, in and of themselves, weather conditions which may cause tremendous destruction. Wildfires, chemicals released by leaks or spills, and radioactive waste released in nuclear accidents are all subject to be worsened by wind. Downed power lines are caused by high winds and flying/falling debris, such as tree limbs, or by ice storms. Floods are generally caused by high rainfall amounts. Thus, the weather conditions during these accidents and disasters are responsible for much of the associated danger and service outages.
Weather prediction should be an integral part of Emergency Response Management—knowing the present and future weather conditions can help Emergency Response Management to send resources not only to locations that are in need. However, current predictive techniques for weather patterns are generally performed on a national, or multi-state regional scale. This large scale weather prediction is largely insufficient for predicting the location of specific weather conditions with enough precision to ultimately assist Emergency Response Management personnel. Prior art weather monitoring stations are generally spaced 100-200 miles apart due to their expense and complexity. The wide spacing and skyward focus of these monitoring stations largely prevents them from monitoring ground conditions, and provides weather data accurate enough only to predict general weather patterns.
Many kinds of threats that the utility industry, natural resource managers, emergency responders and government agencies face are created and/or exacerbated by weather conditions. In particular threats created by high winds, icing, and lightning strikes are among the most difficult to predict and assess, because of their highly time-dependent geospatial distribution. The threat posed by the dispersion of chemicals released by leaks or spills, and radioactive waste released in nuclear accidents is also difficult to predict and assess, because of the highly time and space dependent weather conditions. Floods are generally caused by high rainfall amounts. Thus, the highly variable spatial and temporal variability of weather conditions play a crucial role in how the utility industry and government agencies respond.
The utility industry knows that weather plays in the long-term management of resources and has used long-term weather forecasts to plan the distribution of resources. However, the use of high spatial and temporal resolution short-term forecasts pin-pointed at specific regions has not been explored by either the utility industry or the government agencies charged with responding to natural or man-made disasters. Current weather numerical weather forecasts are generally performed on a national, or multi-state regional scale. This large-scale weather prediction is largely insufficient for predicting with enough precision the location and severity of weather conditions to provide actionable intelligence to the utility industry or emergency responders. Again, as noted above, prior art weather monitoring stations are generally spaced 100-200 miles apart due to their expense and complexity. The wide spacing of these monitoring stations largely prevents them from providing weather data at the local or neighborhood level, which is crucial for the utility industry and emergency responders.
The national or multi-state regional forecasts are insufficient for more localized weather prediction needed during an emergency. Detailed information about the character of the wind field over a neighborhood could be the difference between whether a school can be evacuated in time to avoid a poisonous chemical cloud, or whether it would be better for students to remain inside. Further, a difference of just a few degrees in temperature or a few miles per hour of wind speed over a distance of less than a mile could be the difference between a few power lines being downed versus hundreds of thousands of people in a heavily populated area being without power.
Also, providing alerts for weather events already in progress is not most effective from an early warning perspective. Accurate localized weather prediction and effective early warning is lacking. Accurate weather prediction can also be important on an individualized basis. For example, if there is a tornado threat in a localized area, predictive weather alerts can be important to give accurate and advance notice of the threat. Accuracy is important because several false alarms can cause an individual to no longer give credence to the alert, which over time can result in deaths or injuries Better accuracy is important and now-casts are often too late. If accurate localized weather prediction and effective early warning is provided, it can have a positive impact on safety management and significantly impact everyday individual lifestyles by allowing an individual to better plan their daily activities in consideration of the likely weather conditions. Outdoor activities such as weddings, camping, hiking, cycling, golfing and others can be better managed.
Accordingly, embodiments of the present invention provide for a system and method that monitors environmental conditions at locations spaced closer together than prior art monitoring stations. Thus, the greater number of monitoring stations in a smaller area provide for a higher resolution of weather data, allowing for a more precise and accurate forecast of conditions including weather data closer to the ground. When such weather data is correlated with relatively static infrastructure data, the present invention allows for a threat level index to be created which indicates the localities which are most likely to be threatened by the exacerbation of an event by the weather. Emergency Response Management may then use the threat level index to determine where and when to martial personnel and on an individualized basis an individual can make be provide with information to make better choices.